Profile photo Nico van den Berg

Nico van den Berg

Full Professor

Strategic program(s):

Biography

Professor Nico van den Berg is the head of the Computational Imaging group for MRI diagnostics and therapy of the Centre of Image Sciences at the UMC Utrecht. The Computational Imaging group covers all aspects of the MRI workflow for diagnostics and therapy, from first principles modelling and hardware engineering to translating new MRI methods into clinic. For this purpose we draw on expertise and advances from the fields of (MR) physics, mathematics, computing and artificial intelligence.

One important research line is the exploration of next generation techniques to make MRI exams much shorter, reduce patient discomfort and therefore also increase robustness and diagnostic quality. An example of this is the MR-STAT technique developed within the group that can deliver quantitative MRI information based on raw time-domain signals in a fraction of scan time.
Moreover, within my group we have a large research activity on the use of MRI for radiation therapy. This includes 3D motion tracking of moving targets in  MR guided radiation delivery, MRI-only radiation planning and deep learning image processing applications for radiation therapy.

We pay considerable attention to translate our work to actual (clinical) usage in radiotherapy and radiology/ Currently, the group consists of three senior staff members, one computer scientist, four post-docs and eight PhD students.

Prof. Van den Berg is one of the coordinators of the UMC Utrecht accelerator Image guided interventions and the UMCU AI lab for Imaging and Image guided interventions.

 

Prof. Van den Berg has a 0.9 fte position at the UMC Utrecht. Prof. Van den Berg is co-founder and a minority shareholder of UMCU spin off PrecorDx.  PrecorDx is the winner of the NWO Venture Challenge 2022.

 

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Research groups

Cardiovascular imaging and image guided treatment

Research aim

By developing and implementing advanced imaging and AI, we aim to enhance individualized detection, prediction, and minimally invasive treatment of cardiovascular disease.This optimizes patient selection, treatment guidance, and clinical outcomes.

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Recent publications

Automated pelvic MRI measurements associated with urinary incontinence for prostate cancer patients undergoing radical prostatectomy Ingeborg van den Berg, Robert N Spaans, Frank J Wessels, Erik J R J van der Hoeven, Charlotte J Tutein Nolthenius, Roderick C N van den Bergh, Jochem R N van der Voort van Zyp, Cornelis A T van den Berg, Harm H E van Melick
European radiology experimental, 2024, vol. 8
High SNR full brain relaxometry at 7T by accelerated MR-STAT Edwin Versteeg, Hongyan Liu, Oscar van der Heide, Miha Fuderer, Cornelis A T van den Berg, Alessandro Sbrizzi
Magnetic Resonance in Medicine, 2024, vol. 92, p.226-235
Water diffusion and T2 quantification in transient-state MRI Miha Fuderer, Oscar van der Heide, Hongyan Liu, Cornelis A T van den Berg, Alessandro Sbrizzi
NMR in Biomedicine, 2024, vol. 37
Deep-learning-based joint rigid and deformable contour propagation for magnetic resonance imaging-guided prostate radiotherapy Iris D Kolenbrander, Matteo Maspero, Allard A Hendriksen, Ryan Pollitt, Jochem R N van der Voort van Zyp, Cornelis A T van den Berg, Josien P W Pluim, Maureen A J M van Eijnatten
Medical Physics, 2024, vol. 51, p.2367-2377
GPU-accelerated Bloch simulations and MR-STAT reconstructions using the Julia programming language Oscar van der Heide, Cornelis A T van den Berg, Alessandro Sbrizzi
Magnetic Resonance in Medicine, 2024, vol. 92, p.618-630
Generalizable synthetic MRI with physics-informed convolutional networks Luuk Jacobs, Stefano Mandija, Hongyan Liu, Cornelis A T van den Berg, Alessandro Sbrizzi, Matteo Maspero
Medical Physics, 2023, vol. 51, p.3348-3359

External positions

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